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Fix CRAN submission (#6076)

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Tong He 2020-09-02 14:38:27 +08:00 committed by GitHub
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10 changed files with 23 additions and 19 deletions
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2
R-package/DESCRIPTION
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@ -2,7 +2,7 @@ Package: xgboost
Type: Package
Title: Extreme Gradient Boosting
Version: 1.2.0.1
Date: 2020-02-21
Date: 2020-08-28
Authors@R: c(
person("Tianqi", "Chen", role = c("aut"),
email = "tianqi.tchen@gmail.com"),

2
R-package/R/utils.R
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@ -349,6 +349,7 @@ NULL
#' # Save as a stand-alone file (JSON); load it with xgb.load()
#' xgb.save(bst, 'xgb.model.json')
#' bst2 <- xgb.load('xgb.model.json')
#' if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
#'
#' # Save as a raw byte vector; load it with xgb.load.raw()
#' xgb_bytes <- xgb.save.raw(bst)
@ -364,6 +365,7 @@ NULL
#' obj2 <- readRDS('my_object.rds')
#' # Re-construct xgb.Booster object from the bytes
#' bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
#' if (file.exists('my_object.rds')) file.remove('my_object.rds')
#'
#' @name a-compatibility-note-for-saveRDS-save
NULL

2
R-package/R/xgb.cv.R
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@ -79,7 +79,7 @@
#'
#' All observations are used for both training and validation.
#'
#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
#' Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
#'
#' @return
#' An object of class \code{xgb.cv.synchronous} with the following elements:

8
R-package/R/xgb.train.R
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@ -130,16 +130,16 @@
#' Note that when using a customized metric, only this single metric can be used.
#' The following is the list of built-in metrics for which Xgboost provides optimized implementation:
#' \itemize{
#' \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
#' \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
#' \item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
#' \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#' By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
#' Different threshold (e.g., 0.) could be specified as "error@0."
#' \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#' \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
#' \item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
#' \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
#' }
#'
#' The following callbacks are automatically created when certain parameters are set:

2
R-package/man/a-compatibility-note-for-saveRDS-save.Rd
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@ -43,6 +43,7 @@ bst2 <- xgb.load('xgb.model')
# Save as a stand-alone file (JSON); load it with xgb.load()
xgb.save(bst, 'xgb.model.json')
bst2 <- xgb.load('xgb.model.json')
if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
# Save as a raw byte vector; load it with xgb.load.raw()
xgb_bytes <- xgb.save.raw(bst)
@ -58,5 +59,6 @@ saveRDS(obj, 'my_object.rds')
obj2 <- readRDS('my_object.rds')
# Re-construct xgb.Booster object from the bytes
bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
if (file.exists('my_object.rds')) file.remove('my_object.rds')
}

2
R-package/man/xgb.cv.Rd
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@ -154,7 +154,7 @@ The cross-validation process is then repeated \code{nrounds} times, with each of
All observations are used for both training and validation.
Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
}
\examples{
data(agaricus.train, package='xgboost')

8
R-package/man/xgb.train.Rd
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@ -215,16 +215,16 @@ User may set one or several \code{eval_metric} parameters.
Note that when using a customized metric, only this single metric can be used.
The following is the list of built-in metrics for which Xgboost provides optimized implementation:
\itemize{
\item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
\item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
\item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
Different threshold (e.g., 0.) could be specified as "error@0."
\item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
\item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
\item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
\item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
\item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
\item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
}
The following callbacks are automatically created when certain parameters are set:

12
R-package/vignettes/discoverYourData.Rmd
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@ -57,7 +57,7 @@ To answer the question above we will convert *categorical* variables to `numeric
In this Vignette we will see how to transform a *dense* `data.frame` (*dense* = few zeroes in the matrix) with *categorical* variables to a very *sparse* matrix (*sparse* = lots of zero in the matrix) of `numeric` features.
The method we are going to see is usually called [one-hot encoding](http://en.wikipedia.org/wiki/One-hot).
The method we are going to see is usually called [one-hot encoding](https://en.wikipedia.org/wiki/One-hot).
The first step is to load `Arthritis` dataset in memory and wrap it with `data.table` package.
@ -66,7 +66,7 @@ data(Arthritis)
df <- data.table(Arthritis, keep.rownames = FALSE)
```
> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](http://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
> `data.table` is 100% compliant with **R** `data.frame` but its syntax is more consistent and its performance for large dataset is [best in class](https://stackoverflow.com/questions/21435339/data-table-vs-dplyr-can-one-do-something-well-the-other-cant-or-does-poorly) (`dplyr` from **R** and `Pandas` from **Python** [included](https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A-Grouping)). Some parts of **Xgboost** **R** package use `data.table`.
The first thing we want to do is to have a look to the first few lines of the `data.table`:
@ -137,8 +137,8 @@ levels(df[,Treatment])
#### Encoding categorical features
Next step, we will transform the categorical data to dummy variables.
Several encoding methods exist, e.g., [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) is a common approach.
We will use the [dummy contrast coding](http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm#dummy) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
Several encoding methods exist, e.g., [one-hot encoding](https://en.wikipedia.org/wiki/One-hot) is a common approach.
We will use the [dummy contrast coding](https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.
@ -176,7 +176,7 @@ bst <- xgboost(data = sparse_matrix, label = output_vector, max_depth = 4,
You can see some `train-error: 0.XXXXX` lines followed by a number. It decreases. Each line shows how well the model explains your data. Lower is better.
A model which fits too well may [overfit](http://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
A model which fits too well may [overfit](https://en.wikipedia.org/wiki/Overfitting) (meaning it copy/paste too much the past, and won't be that good to predict the future).
> Here you can see the numbers decrease until line 7 and then increase.
>
@ -304,7 +304,7 @@ Linear model may not be that smart in this scenario.
Special Note: What about Random Forests™?
-----------------------------------------
As you may know, [Random Forests™](http://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](http://en.wikipedia.org/wiki/Ensemble_learning) family.
As you may know, [Random Forests™](https://en.wikipedia.org/wiki/Random_forest) algorithm is cousin with boosting and both are part of the [ensemble learning](https://en.wikipedia.org/wiki/Ensemble_learning) family.
Both trains several decision trees for one dataset. The *main* difference is that in Random Forests™, trees are independent and in boosting, the tree `N+1` focus its learning on the loss (<=> what has not been well modeled by the tree `N`).

2
R-package/vignettes/xgboost.bib
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@ -24,7 +24,7 @@
author = "K. Bache and M. Lichman",
year = "2013",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
url = "http://archive.ics.uci.edu/ml/",
institution = "University of California, Irvine, School of Information and Computer Sciences"
}

2
R-package/vignettes/xgboostPresentation.Rmd
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@ -68,7 +68,7 @@ The version 0.4-2 is on CRAN, and you can install it by:
install.packages("xgboost")
```
Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost)
Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost/)
## Learning